Why should you move to Cloud for Data Warehousing?

So far technology has been taking us for a spin, we have been drowning under that mass called data, if that is not enough Big Data keeps pulling us 5 ways (volume, velocity, variety, veracity, and value).

Not painting a great picture am I, but then a host of Data engineers, Data Scientists, and infrastructure support members came to everyone’s rescue, helping us ensure that the integration between systems is flawless. But we haven’t gone that far have we? Still talking to the wall instead of the engineering team, too many touch points, fragmented environments resulting in lack of cohesive monitoring, silos of data and Meta data, and an incoherent security architecture.

 

The data and analytics workload continues to move in large numbers from private on-premises data centers to public cloud environments. Organizations try their best to reduce expenditures like those incurred procuring hardware and keep hoping to soon reach that point so they can speed, scale, and be agile.

So how prepared is your organization to address the role of cloud in their lives? How prepared are you to increase your efficiency, improve your cash flow, and lower your operational costs?

 

What is data warehousing?

Data warehousing, simply put allows you to electronically store ginormous amounts of information. So usually, this system is used for reporting and data analysis purposes- in short what those in BI (business intelligence) use extensively. You can store historical data in these warehouses by integrating transaction data from various sources.

Data warehousing process

 

Why to move to Cloud for data warehousing 3 reasons?

So using a cloud platform for data warehousing, speaking in plain English, means using a platform that allows you to securely share and consume shared data. It will provide you with services like servers, storage, databases, networking, data analytics, and intelligence on the cloud so you can innovate faster, and scale quicker.

Here are the top 3 reasons why moving to cloud is a good idea

 

Cloud Data warehousing Reason #1:  Cloud helps with better collaboration

I wouldn’t give the credit as much to cloud as to COVID for better team collaboration in these times. But yes, Cloud does help you collaborate better on shared data with your teams, and one major reason is because you can access it anywhere, and using multiple devices.

Cloud Data warehousing Reason #2: Reduced costs

Storing data physically is a tad expensive and expanding your data footprint is going to be tough with cables attached. In order that your analytical needs are met, you scale independently, compute as much as you want, and store perfectly, choosing a Cloud data warehouse is what will work best.

Cloud Data warehousing Reason #3:  Security

And you thought having things right where you can see this is secure. Digital copies are more secure now more than physical copies, so having your data warehouse in the cloud is any day more secure than an on premise system.  It is important that you choose the right cloud platform and the right business model that stresses more on encryption and security.

And here is another reason I think, that is as important, Cloud can help you simplify your infrastructure and reduce your capital investments since you then use even more services linked to data warehouse like data management, business intelligence, etc.

Now that you have the reasons on why you should go the cloud route when it comes to data warehousing.

Let’s take the next step, and find out in what way can a data analytics platform help you solve your problems with data and ship you off to cloud in a much faster and seamless way?

 

What should you expect from a Data Analytics platform?

Not much apart from it being able to:

  • Boost analytics productivity for all users and workloads without resource contention
  • Help you scale instantly through unlimited storage and compute capacity while easily matching business needs
  • Onboard and support a diverse set of data without bottleneck delays to develop insights and produce answers from the data as expeditiously as possible.

That sure does answer some questions people have, like maybe do I really want a data warehouse on the cloud?

Let me take you a step further into that question, you need to choose the right cloud data platform so that your data warehousing, data lakes, data engineering, and Data Science needs are taken care of correctly.

What can Snowflake Cloud Data Platform do?

So a cloud data platform as I was saying can give you an infinite amount of flexibility, analyze better and securely share your data. Since it has a multi-cluster shared data architecture, all your data can be accessed by you virtually without your performance being impacted.  Being on one single platform makes Snowflake the right choice for you since security, performance, and maintenance can all happen in one place.

Benefits of Snowflake Cloud Data Platform

 

 

Criteria

      ❄


Snowflake

 

Shared Data

(Traditional DW)

 

Shared Nothing (Netezza/Redshift)

 

Hub and Spoke

   (S3 +Athens)

Cloud Query Engines (Athena)  

Why Important?

Low-latency, high- performance across simple and complex queries  

Yes

 

Yes

 

No

 

Limited

 

Delivers faster business results

Easily join structured and semi-structured data for analytics  

Limited

 

Limited

 

More complex

 

Limited

 

Enables 360o views, multi-channel data analytics

Unlimited query concurrency within a single, integrated platform  

Limited

 

Limited

 

No

 

Limited

 

Eliminates data duplication costs and complexity

Unlimited connections within a single, integrated platform  

 

 

Limited

 

 

Limited

 

 

No

 

 

Limited

Supports as many accesses to platform as needed, while avoiding data duplication
 

Multi-statement ACID transactions

 

Yes

 

Yes

 

No

 

No

Assures pipeline and transactional integrity for single and multi- table inserts
Workload isolation, within the same integrated platform and single source of truth  

 

 

No

 

 

No

 

 

No

 

 

No

 

Matches resources to business needs with maximum simplicity

 

Instantly scale up, down, out or in without disruption

 

 

 

No

 

 

Limited

 

Very Limited

 

 

Limited

Delivers fast execution and ensures environment stays up and running when scaling
Simple management, e.g., no indexing, no manual partitioning  

No

 

No

 

No

 

Yes

 

Enables data teams to attain higher strategic focus

 

 

ZeroCopy Cloning

 

 

 

No

 

 

No

 

 

No

 

 

No

Simplifies test-dev with full production data, while eliminating storage duplication
 

Cross-cloud operation

 

No

 

No

 

No

 

No

Provides strategic and operational flexibility across clouds

 

 

So, using the right solutions expert, can help provide end user access to raw data sets in Snowflake, and your data engineers can focus on activities that increase analytics, including integrating more data sets.  Snowflake’s pay-per-second model offers significant cost savings compared to the instance-based pricing of traditional data analytics solutions.

You can maximize your return on investment with single analytics repository & gain richer insights for a fraction of the cost and effort if you use the right cloud platform. It is important that you have the right Software solutions company providing you with a scalable solution so you can experience fast and reliable system performance at a low cost.

 

This is perhaps reason enough for you to invest in a cloud data warehouse, right about now. Write to us, to learn more about our services, and to learn how we can help you overcome barriers to scalable data analysis and data science.

 

 

About Abhishek Tanwade

Associate Architect – BI

  • Cloud
  • Big Data
Abhishek Tanwade is an Associate Architect – BI at Nitor Infotech with over 6 years of experience as an AWS Solution Architect (associate certified professional). In his 12 years long career, Abhishek has spearheaded different Data Engineering & Analytics projects thereby gaining experience on various proprietary technologies. He is a distinguished Data Consolidation Patterns expert having knowledge about various Data leveraging technologies. He is a techno-synergist equipped with a strong sense of how Relational Databases work. His experience in visualization tools like Power BI, QlikView, and AWS Quicksight sets him apart. His horizon of expertise expands to enabling Cloud-based ETL services such as AWS glue, Data Pipeline, and Azure Data Factory. Abhishek’s team members vouch that his ever-smiling nature makes for a happier and positive work atmosphere. His personality and great business acumen help them to stay focused and motivates them to aim higher.